Natural language processing (NLP) identifies entities or objects in unstructured text of a document and determines relationships between the entities. An NLP engine identifies the entities or objects and variations of the entities or objects by matching tokens or words in the unstructured text to entries in a dictionary containing key terms and variations of the key terms. The corresponding dictionary entries represent the entities or objects in the unstructured text. A person makes a limited, inflexible Boolean decision as to whether an annotation or concept based on the matched entries should be applied to the tokens or words.
U.S. Pat. No. 8,332,434 to Salkeld et al. teaches a system to map a set of words to a set of ontology terms. A term set corresponding to a set of words in an ontology context is determined for different starting points of ontology contexts. The term sets acquired from each of the starting points are ranked using a goodness function considering both consistency and popularity. A term which has a very high term rank is degraded or discarded if its ontology has a trivial correlation with the starting point ontology.